Triple

T21398826
Position Surface form Disambiguated ID Type / Status
Subject Salome, Where She Danced E527857 entity
Predicate storyBy P1955 FINISHED
Object Michael Fessier NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Michael Fessier | Statement: [Salome, Where She Danced, storyBy, Michael Fessier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Fessier
Context triple: [Salome, Where She Danced, storyBy, Michael Fessier]
  • A. Michael Fessier chosen
    Michael Fessier was an American screenwriter and author known for his work on Hollywood films in the 1930s and 1940s, often contributing to romantic comedies and musicals.
  • B. Michael Ferris
    Michael Ferris is a cinematographer known for his work on the film "Point of No Return."
  • C. Michael Ferris
    Michael Ferris is an American screenwriter best known for co-writing major studio films such as "Terminator Salvation" and "The Net."
  • D. Michael LeSieur
    Michael LeSieur is an American screenwriter known for his work on comedy films, including co-writing the 2018 animated adaptation of Dr. Seuss's "The Grinch."
  • E. Patrick Fischler
    Patrick Fischler is an American character actor known for his memorable supporting roles in film and television, including appearances in projects like Mulholland Drive, Mad Men, and Lost.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e0b520ee3c8190abddbee7e37e834c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69ee62cf3e808190847ad66d2e65f9f2 completed April 26, 2026, 7:09 p.m.
Created at: April 16, 2026, 5:14 p.m.